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  <div class="section" id="numpy-amax">
<h1>numpy.amax<a class="headerlink" href="#numpy-amax" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.amax">
<code class="sig-prename descclassname">numpy.</code><code class="sig-name descname">amax</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">axis=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">keepdims=&lt;no value&gt;</em>, <em class="sig-param">initial=&lt;no value&gt;</em>, <em class="sig-param">where=&lt;no value&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/core/fromnumeric.py#L2551-L2668"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.amax" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the maximum of an array or maximum along an axis.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Input data.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">None or int or tuple of ints, optional</span></dt><dd><p>Axis or axes along which to operate.  By default, flattened input is
used.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.7.0.</span></p>
</div>
<p>If this is a tuple of ints, the maximum is selected over multiple axes,
instead of a single axis or all the axes as before.</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, optional</span></dt><dd><p>Alternative output array in which to place the result.  Must
be of the same shape and buffer length as the expected output.
See <em class="xref py py-obj">ufuncs-output-type</em> for more details.</p>
</dd>
<dt><strong>keepdims</strong><span class="classifier">bool, optional</span></dt><dd><p>If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.</p>
<p>If the default value is passed, then <em class="xref py py-obj">keepdims</em> will not be
passed through to the <a class="reference internal" href="#numpy.amax" title="numpy.amax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">amax</span></code></a> method of sub-classes of
<a class="reference internal" href="numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a>, however any non-default value will be.  If the
sub-class’ method does not implement <em class="xref py py-obj">keepdims</em> any
exceptions will be raised.</p>
</dd>
<dt><strong>initial</strong><span class="classifier">scalar, optional</span></dt><dd><p>The minimum value of an output element. Must be present to allow
computation on empty slice. See <a class="reference internal" href="numpy.ufunc.reduce.html#numpy.ufunc.reduce" title="numpy.ufunc.reduce"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reduce</span></code></a> for details.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.15.0.</span></p>
</div>
</dd>
<dt><strong>where</strong><span class="classifier">array_like of bool, optional</span></dt><dd><p>Elements to compare for the maximum. See <a class="reference internal" href="numpy.ufunc.reduce.html#numpy.ufunc.reduce" title="numpy.ufunc.reduce"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reduce</span></code></a>
for details.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.17.0.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>amax</strong><span class="classifier">ndarray or scalar</span></dt><dd><p>Maximum of <em class="xref py py-obj">a</em>. If <em class="xref py py-obj">axis</em> is None, the result is a scalar value.
If <em class="xref py py-obj">axis</em> is given, the result is an array of dimension
<code class="docutils literal notranslate"><span class="pre">a.ndim</span> <span class="pre">-</span> <span class="pre">1</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.amin.html#numpy.amin" title="numpy.amin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">amin</span></code></a></dt><dd><p>The minimum value of an array along a given axis, propagating any NaNs.</p>
</dd>
<dt><a class="reference internal" href="numpy.nanmax.html#numpy.nanmax" title="numpy.nanmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanmax</span></code></a></dt><dd><p>The maximum value of an array along a given axis, ignoring any NaNs.</p>
</dd>
<dt><a class="reference internal" href="numpy.maximum.html#numpy.maximum" title="numpy.maximum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">maximum</span></code></a></dt><dd><p>Element-wise maximum of two arrays, propagating any NaNs.</p>
</dd>
<dt><a class="reference internal" href="numpy.fmax.html#numpy.fmax" title="numpy.fmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fmax</span></code></a></dt><dd><p>Element-wise maximum of two arrays, ignoring any NaNs.</p>
</dd>
<dt><a class="reference internal" href="numpy.argmax.html#numpy.argmax" title="numpy.argmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">argmax</span></code></a></dt><dd><p>Return the indices of the maximum values.</p>
</dd>
</dl>
<p><a class="reference internal" href="numpy.nanmin.html#numpy.nanmin" title="numpy.nanmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanmin</span></code></a>, <a class="reference internal" href="numpy.minimum.html#numpy.minimum" title="numpy.minimum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">minimum</span></code></a>, <a class="reference internal" href="numpy.fmin.html#numpy.fmin" title="numpy.fmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fmin</span></code></a></p>
</div>
<p class="rubric">Notes</p>
<p>NaN values are propagated, that is if at least one item is NaN, the
corresponding max value will be NaN as well. To ignore NaN values
(MATLAB behavior), please use nanmax.</p>
<p>Don’t use <a class="reference internal" href="#numpy.amax" title="numpy.amax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">amax</span></code></a> for element-wise comparison of 2 arrays; when
<code class="docutils literal notranslate"><span class="pre">a.shape[0]</span></code> is 2, <code class="docutils literal notranslate"><span class="pre">maximum(a[0],</span> <span class="pre">a[1])</span></code> is faster than
<code class="docutils literal notranslate"><span class="pre">amax(a,</span> <span class="pre">axis=0)</span></code>.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span>
<span class="go">array([[0, 1],</span>
<span class="go">       [2, 3]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">amax</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>           <span class="c1"># Maximum of the flattened array</span>
<span class="go">3</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">amax</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>   <span class="c1"># Maxima along the first axis</span>
<span class="go">array([2, 3])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">amax</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>   <span class="c1"># Maxima along the second axis</span>
<span class="go">array([1, 3])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">amax</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">where</span><span class="o">=</span><span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">],</span> <span class="n">initial</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([-1,  3])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">b</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">amax</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="go">nan</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">amax</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">where</span><span class="o">=~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">b</span><span class="p">),</span> <span class="n">initial</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="go">4.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="go">4.0</span>
</pre></div>
</div>
<p>You can use an initial value to compute the maximum of an empty slice, or
to initialize it to a different value:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([[</span><span class="o">-</span><span class="mi">50</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">]],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([ 0, 10])</span>
</pre></div>
</div>
<p>Notice that the initial value is used as one of the elements for which the
maximum is determined, unlike for the default argument Python’s max
function, which is only used for empty iterables.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="mi">5</span><span class="p">],</span> <span class="n">initial</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
<span class="go">6</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">max</span><span class="p">([</span><span class="mi">5</span><span class="p">],</span> <span class="n">default</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
<span class="go">5</span>
</pre></div>
</div>
</dd></dl>

</div>


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